Differentiation device, differentiation method for depression symptoms, determination method for level of depression symptoms, stratification method for depression patients, determination method for effects of treatment of depression symptoms, and brain activity training device

A technology for distinguishing devices and brain activities, which can be used in diagnosis, psychotherapy, image analysis, etc., and can solve problems such as insufficient consideration of functional connections

Pending Publication Date: 2020-07-24
ATR ADVANCED TELECOMM RES INST INT +1
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  • Summary
  • Abstract
  • Description
  • Claims
  • Application Information

AI Technical Summary

Problems solved by technology

However, state-of-the-art rs-fc MRI methods examine the activity of global neural networks (e.g., the aforementioned default mode network) and currently do not adequately account for more detailed functional connectivity

Method used

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  • Differentiation device, differentiation method for depression symptoms, determination method for level of depression symptoms, stratification method for depression patients, determination method for effects of treatment of depression symptoms, and brain activity training device
  • Differentiation device, differentiation method for depression symptoms, determination method for level of depression symptoms, stratification method for depression patients, determination method for effects of treatment of depression symptoms, and brain activity training device
  • Differentiation device, differentiation method for depression symptoms, determination method for level of depression symptoms, stratification method for depression patients, determination method for effects of treatment of depression symptoms, and brain activity training device

Examples

Experimental program
Comparison scheme
Effect test

Embodiment 1

[0600] II. Example 1: Selection of 12 pairs of functional connectivity for classification of melancholic MDD

[0601] To select the 12 pairs of functional connectivity for the classification of depressive MDD, the rs-fMRI data of 66 depressive MDD patients and 66 healthy individuals shown in Table 1a were used. Selected for classifying the melancholic MDD group based on the procedure described in this embodiment, according to the method for creating a classifier for classifying autism disorder (ASD) as reported by Yahata et al. above. functional connections.

[0602] The system uses sparse canonical correlation analysis (L1-SCCA) with L1 regularization and sparse logistic regression (SLR). SLR is not used to classify MDD, but has the ability to train logistic regression models while pruning individual functional connections in an objective manner. Before training via SLR, a certain amount of input is reduced by L1-SCCA, while reducing the impact of redundant variables (NV) t...

Embodiment 2

[0620] III. Example 2: Application of the Classifier of the Invention to the Non-Melancholic MDD Group and the Whole MDD Group

[0621] Figure 27 c shows the data that the classifier of the present invention was created by using the entire MDD group, the melancholic MDD group and the non-melancholic MDD group each as training data (vertical direction), and using each group as test data (horizontal direction) to consider the accuracy obtained. As a result of LOOCV, for example, the classifier generated from the melancholic MDD panel had an accuracy of 54% for non-melancholic MDD (sensitivity: 42%, specificity: 67%, AUC: 0.65). The accuracy of LOOCV for the entire MDD group was 66% (sensitivity: 58%, specificity: 74%, AUC: 0.74).

[0622] The results show the fact that the classifier trained on the entire MDD group has lower classification accuracy than the classifier trained on the melancholic MDD group or the classifier trained on the melancholic MDD group. Furthermore, th...

Embodiment 3

[0624] IV. Example 3: Evaluation of Severity of Depression Using WLS Score and Evaluation of Treatment Effect

[0625] The correlation between BD scores and WLS scores was checked to investigate whether WLS scores are associated with depression severity. Such as Figure 31 As shown in a, when calculating the correlation between BDI score and WLS score for the group including the whole MDD group and the healthy control group, a value of r=0.655 and a value of p=0.001 in the permutation test were obtained at n=186 value, which indicates that the two scores are related to each other. Furthermore, when focusing only on the entire MDD group ( Figure 31 For b), a value of r = 0.188 and a value of p = 0.046 in the permutation test were obtained at n = 93, indicating that these two scores are relatively correlated with each other.

[0626] Next, it was considered whether the effect of escitalopram, which is a selective serotonin reuptake inhibitor (SSRI), can be evaluated by the W...

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Abstract

The present invention differentiates objective disease labels for depression symptoms relative to brain activity states. As one means to address the problem to be addressed thereby, the present invention provides a differentiation device that is for helping to determine whether a subject has depression symptoms. The differentiation device comprises a storage device that is for storing informationthat specifies a classifier that has been generated by classifier generation processing from signals obtained by using a brain activity detection device to chronologically premeasure signals that indicate brain activity in a plurality of prescribed regions of the resting brains of each of a plurality of participants that include healthy and depressive patients. The classifier: is generated on thebasis of a weighted sum of a plurality of functional connectivities that, from among functional connectivities between the plurality of prescribed regions, have, by means of machine learning, been selected by feature selection as being related to disease labels for depression symptoms; and is generated to differentiate the disease labels for depression symptoms. The differentiation device also comprises a computing device. Using the classifier, the computing device executes differentiation processing that generates classification results for the depression symptoms of the subject.

Description

technical field [0001] The invention relates to a discriminating device, a method for discriminating depression symptoms, a method for judging the level of depression symptoms, a method for classifying patients with depression, a method for judging the therapeutic effect of depression symptoms and a brain activity training device. Background technique [0002] (biomarkers) [0003] An index used to quantify biological information to quantitatively grasp biological changes in a living body is called a "biomarker". [0004] The US Food and Drug Administration (FDA) defines a biomarker as "a property that is measured as an indicator of a normal biological process, a disease-causing process, or a response to an exposure or intervention, including therapeutic intervention." In addition, biomarkers that characterize the state or degree of change or cure of a disease are used as surrogate markers (surrogate markers) for verifying the effectiveness of new drugs in clinical trials. ...

Claims

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Application Information

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Patent Type & Authority Applications(China)
IPC IPC(8): A61B5/055
CPCA61B5/055G06T7/0012G06T2207/10088G06T2207/20081G06T2207/20084G06T2207/30016G16H70/60G16H30/20G16H50/30G16H50/70G16H20/70G06N20/00G06T7/0014G06F18/2431G06F18/214
Inventor 朱塞佩·里斯森本淳川人光男山田贵志市川奈穗冈本泰昌
Owner ATR ADVANCED TELECOMM RES INST INT
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